[{"_id":"14107","day":"23","publication":"36th Conference on Neural Information Processing Systems","title":"Self-supervised amodal video object segmentation","oa":1,"language":[{"iso":"eng"}],"author":[{"first_name":"Jian","full_name":"Yao, Jian","last_name":"Yao"},{"last_name":"Hong","full_name":"Hong, Yuxin","first_name":"Yuxin"},{"last_name":"Wang","full_name":"Wang, Chiyu","first_name":"Chiyu"},{"last_name":"Xiao","full_name":"Xiao, Tianjun","first_name":"Tianjun"},{"last_name":"He","first_name":"Tong","full_name":"He, Tong"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"},{"last_name":"Wipf","full_name":"Wipf, David","first_name":"David"},{"first_name":"Yanwei","full_name":"Fu, Yanwei","last_name":"Fu"},{"first_name":"Zheng","full_name":"Zhang, Zheng","last_name":"Zhang"}],"external_id":{"arxiv":["2210.12733"]},"department":[{"_id":"FrLo"}],"citation":{"ista":"Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z. 2022. Self-supervised amodal video object segmentation. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","apa":"Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In <i>36th Conference on Neural Information Processing Systems</i>. New Orleans, LA, United States. <a href=\"https://doi.org/10.48550/arXiv.2210.12733\">https://doi.org/10.48550/arXiv.2210.12733</a>","ama":"Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: <i>36th Conference on Neural Information Processing Systems</i>. ; 2022. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.12733\">10.48550/arXiv.2210.12733</a>","mla":"Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” <i>36th Conference on Neural Information Processing Systems</i>, 2022, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.12733\">10.48550/arXiv.2210.12733</a>.","short":"J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022.","chicago":"Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.” In <i>36th Conference on Neural Information Processing Systems</i>, 2022. <a href=\"https://doi.org/10.48550/arXiv.2210.12733\">https://doi.org/10.48550/arXiv.2210.12733</a>.","ieee":"J. Yao <i>et al.</i>, “Self-supervised amodal video object segmentation,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2022."},"type":"conference","doi":"10.48550/arXiv.2210.12733","status":"public","oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"New Orleans, LA, United States","end_date":"2022-12-01","name":"NeurIPS: Neural Information Processing Systems","start_date":"2022-11-28"},"publication_status":"published","date_created":"2023-08-21T12:13:25Z","article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2210.12733","open_access":"1"}],"date_published":"2022-10-23T00:00:00Z","abstract":[{"text":"Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervision. To this end, this paper develops a new framework of\r\nSelf-supervised amodal Video object segmentation (SaVos). Our method efficiently leverages the visual information of video temporal sequences to infer the amodal mask of objects. The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning paradigm that efficiently utilizes the visible object parts as the supervision to guide the training on videos. In addition to learning type prior to complete masks for known types, SaVos also learns the spatiotemporal prior, which is also useful for the amodal task and could generalize to unseen types. The proposed\r\nframework achieves the state-of-the-art performance on the synthetic amodal segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends itself well to being transferred to novel distributions using test-time adaptation, outperforming existing models even after the transfer to a new distribution.","lang":"eng"}],"extern":"1","year":"2022","arxiv":1,"date_updated":"2023-09-11T09:34:17Z","month":"10"},{"publication":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition","day":"01","_id":"14114","publication_identifier":{"issn":["1063-6919"],"eissn":["2575-7075"],"isbn":["9781665469470"]},"doi":"10.1109/cvpr52688.2022.01016","oa":1,"department":[{"_id":"FrLo"}],"citation":{"ieee":"D. Zietlow <i>et al.</i>, “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, New Orleans, LA, United States, 2022, pp. 10400–10411.","chicago":"Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner, Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 10400–411. Institute of Electrical and Electronics Engineers, 2022. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">https://doi.org/10.1109/cvpr52688.2022.01016</a>.","mla":"Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">10.1109/cvpr52688.2022.01016</a>.","ama":"Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">10.1109/cvpr52688.2022.01016</a>","short":"D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.","apa":"Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Scholkopf, B., &#38; Russell, C. (2022). Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i> (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">https://doi.org/10.1109/cvpr52688.2022.01016</a>","ista":"Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411."},"type":"conference","publication_status":"published","article_processing_charge":"No","oa_version":"Preprint","extern":"1","abstract":[{"lang":"eng","text":"Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups."}],"year":"2022","scopus_import":"1","month":"07","date_updated":"2023-09-11T09:19:14Z","arxiv":1,"publisher":"Institute of Electrical and Electronics Engineers","date_published":"2022-07-01T00:00:00Z","main_file_link":[{"url":"https://arxiv.org/abs/2203.04913","open_access":"1"}],"title":"Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers","quality_controlled":"1","page":"10400-10411","status":"public","external_id":{"arxiv":["2203.04913"]},"author":[{"full_name":"Zietlow, Dominik","first_name":"Dominik","last_name":"Zietlow"},{"first_name":"Michael","full_name":"Lohaus, Michael","last_name":"Lohaus"},{"first_name":"Guha","full_name":"Balakrishnan, Guha","last_name":"Balakrishnan"},{"last_name":"Kleindessner","first_name":"Matthaus","full_name":"Kleindessner, Matthaus"},{"full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"last_name":"Scholkopf","full_name":"Scholkopf, Bernhard","first_name":"Bernhard"},{"last_name":"Russell","first_name":"Chris","full_name":"Russell, Chris"}],"language":[{"iso":"eng"}],"date_created":"2023-08-21T12:18:00Z","conference":{"location":"New Orleans, LA, United States","name":"CVPR: Conference on Computer Vision and Pattern Recognition","end_date":"2022-06-24","start_date":"2022-06-18"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"title":"Neural attentive circuits","publication":"36th Conference on Neural Information Processing Systems","day":"14","_id":"14168","intvolume":"        35","status":"public","external_id":{"arxiv":["2210.08031"]},"author":[{"full_name":"Rahaman, Nasim","first_name":"Nasim","last_name":"Rahaman"},{"last_name":"Weiss","full_name":"Weiss, Martin","first_name":"Martin"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"last_name":"Pal","first_name":"Chris","full_name":"Pal, Chris"},{"full_name":"Bengio, Yoshua","first_name":"Yoshua","last_name":"Bengio"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"full_name":"Li, Li Erran","first_name":"Li Erran","last_name":"Li"},{"last_name":"Ballas","first_name":"Nicolas","full_name":"Ballas, Nicolas"}],"language":[{"iso":"eng"}],"oa":1,"citation":{"ieee":"N. Rahaman <i>et al.</i>, “Neural attentive circuits,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, United States, 2022, vol. 35.","chicago":"Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.” In <i>36th Conference on Neural Information Processing Systems</i>, Vol. 35, 2022.","ama":"Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. ; 2022.","mla":"Rahaman, Nasim, et al. “Neural Attentive Circuits.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, 2022.","short":"N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022.","apa":"Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., … Ballas, N. (2022). Neural attentive circuits. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35). New Orleans, United States.","ista":"Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems,  Advances in Neural Information Processing Systems, vol. 35."},"department":[{"_id":"FrLo"}],"type":"conference","publication_status":"published","date_created":"2023-08-22T13:57:27Z","article_processing_charge":"No","conference":{"location":"New Orleans, United States","name":"NeurIPS: Neural Information Processing Systems","end_date":"2022-12-01","start_date":"2022-11-29"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","abstract":[{"lang":"eng","text":"Recent work has seen the development of general purpose neural architectures\r\nthat can be trained to perform tasks across diverse data modalities. General\r\npurpose models typically make few assumptions about the underlying\r\ndata-structure and are known to perform well in the large-data regime. At the\r\nsame time, there has been growing interest in modular neural architectures that\r\nrepresent the data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution, computationally efficient, and capable of\r\nsample-efficient adaptation to new data. However, they tend to make\r\ndomain-specific assumptions about the data, and present challenges in how\r\nmodule behavior (i.e., parameterization) and connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we introduce a general purpose, yet\r\nmodular neural architecture called Neural Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse connectivity of neural modules\r\nwithout using domain knowledge. NACs are best understood as the combination of\r\ntwo systems that are jointly trained end-to-end: one that determines the module\r\nconfiguration and the other that executes it on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful module configurations on\r\nthe NLVR2 dataset without additional supervision. Quantitatively, we show that\r\nby incorporating modularity in this way, NACs improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference time while losing less than 3%\r\nperformance. Finally, we find NACs to yield competitive results on diverse data\r\nmodalities spanning point-cloud classification, symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming its general purpose\r\nnature."}],"year":"2022","extern":"1","alternative_title":[" Advances in Neural Information Processing Systems"],"volume":35,"month":"10","date_updated":"2023-09-11T09:29:09Z","arxiv":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.08031"}],"date_published":"2022-10-14T00:00:00Z"},{"page":"5221-5285","quality_controlled":"1","title":"Generalization and robustness implications in object-centric learning","language":[{"iso":"eng"}],"external_id":{"arxiv":["2107.00637"]},"author":[{"last_name":"Dittadi","full_name":"Dittadi, Andrea","first_name":"Andrea"},{"last_name":"Papa","full_name":"Papa, Samuele","first_name":"Samuele"},{"last_name":"Vita","full_name":"Vita, Michele De","first_name":"Michele De"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"last_name":"Winther","full_name":"Winther, Ole","first_name":"Ole"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco"}],"status":"public","intvolume":"      2022","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"Baltimore, MD, United States","end_date":"2022-07-23","name":"International Conference on Machine Learning","start_date":"2022-07-17"},"date_created":"2023-08-22T13:59:55Z","volume":2022,"day":"22","_id":"14170","publication":"Proceedings of the 39th International Conference on Machine Learning","department":[{"_id":"FrLo"}],"type":"conference","citation":{"chicago":"Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello. “Generalization and Robustness Implications in Object-Centric Learning.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, 2022:5221–85. ML Research Press, n.d.","ieee":"A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello, “Generalization and robustness implications in object-centric learning,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, vol. 2022, pp. 5221–5285.","apa":"Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., &#38; Locatello, F. (n.d.). Generalization and robustness implications in object-centric learning. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.","ista":"Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 2022, 5221–5285.","short":"A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, n.d., pp. 5221–5285.","ama":"Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 2022. ML Research Press; :5221-5285.","mla":"Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric Learning.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 2022, ML Research Press, pp. 5221–85."},"oa":1,"oa_version":"Preprint","article_processing_charge":"No","publication_status":"submitted","date_published":"2022-07-22T00:00:00Z","main_file_link":[{"url":"https://arxiv.org/abs/2107.00637","open_access":"1"}],"publisher":"ML Research Press","arxiv":1,"date_updated":"2023-09-11T10:08:14Z","month":"07","alternative_title":["PMLR"],"extern":"1","abstract":[{"lang":"eng","text":"The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation metrics and downstream object property prediction. In addition, we study generalization and robustness by investigating the settings where either a single object is out of distribution -- e.g., having an unseen color, texture, or shape -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be useful for\r\ndownstream tasks and generally robust to most distribution shifts affecting objects. However, when the distribution shift affects the input in a less structured manner, robustness in terms of segmentation and downstream task performance may vary significantly across models and distribution shifts. "}],"year":"2022"},{"intvolume":"       162","status":"public","language":[{"iso":"eng"}],"external_id":{"arxiv":["2203.04413"]},"author":[{"last_name":"Rolland","full_name":"Rolland, Paul","first_name":"Paul"},{"full_name":"Cevher, Volkan","first_name":"Volkan","last_name":"Cevher"},{"last_name":"Kleindessner","first_name":"Matthäus","full_name":"Kleindessner, Matthäus"},{"first_name":"Chris","full_name":"Russel, Chris","last_name":"Russel"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"last_name":"Janzing","first_name":"Dominik","full_name":"Janzing, Dominik"},{"full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"quality_controlled":"1","title":"Score matching enables causal discovery of nonlinear additive noise  models","page":"18741-18753","volume":162,"date_created":"2023-08-22T14:00:18Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"start_date":"2022-07-17","end_date":"2022-07-23","name":"International Conference on Machine Learning","location":"Baltimore, MD, United States"},"oa":1,"department":[{"_id":"FrLo"}],"type":"conference","citation":{"ama":"Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 162. ML Research Press; 2022:18741-18753.","mla":"Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 162, ML Research Press, 2022, pp. 18741–53.","short":"P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753.","apa":"Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., &#38; Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise  models. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.","ista":"Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise  models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753.","ieee":"P. Rolland <i>et al.</i>, “Score matching enables causal discovery of nonlinear additive noise  models,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.","chicago":"Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, 162:18741–53. ML Research Press, 2022."},"publication":"Proceedings of the 39th International Conference on Machine Learning","_id":"14171","day":"22","alternative_title":["PMLR"],"extern":"1","abstract":[{"text":"This paper demonstrates how to recover causal graphs from the score of the\r\ndata distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching algorithms as a building block, we show how to design a new generation\r\nof scalable causal discovery methods. To showcase our approach, we also propose\r\na new efficient method for approximating the score's Jacobian, enabling to\r\nrecover the causal graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive with state-of-the-art causal discovery methods while\r\nbeing significantly faster.","lang":"eng"}],"year":"2022","date_updated":"2023-09-11T10:14:20Z","arxiv":1,"month":"07","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.04413"}],"date_published":"2022-07-22T00:00:00Z","publisher":"ML Research Press","publication_status":"published","article_processing_charge":"No","oa_version":"Preprint"},{"_id":"14172","day":"25","publication":"10th International Conference on Learning Representations","quality_controlled":"1","title":"Visual representation learning does not generalize strongly within the  same domain","oa":1,"language":[{"iso":"eng"}],"external_id":{"arxiv":["2107.08221"]},"author":[{"last_name":"Schott","first_name":"Lukas","full_name":"Schott, Lukas"},{"last_name":"Kügelgen","first_name":"Julius von","full_name":"Kügelgen, Julius von"},{"last_name":"Träuble","first_name":"Frederik","full_name":"Träuble, Frederik"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"full_name":"Russell, Chris","first_name":"Chris","last_name":"Russell"},{"first_name":"Matthias","full_name":"Bethge, Matthias","last_name":"Bethge"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco"},{"full_name":"Brendel, Wieland","first_name":"Wieland","last_name":"Brendel"}],"type":"conference","department":[{"_id":"FrLo"}],"citation":{"ieee":"L. Schott <i>et al.</i>, “Visual representation learning does not generalize strongly within the  same domain,” in <i>10th International Conference on Learning Representations</i>, Virtual, 2022.","chicago":"Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” In <i>10th International Conference on Learning Representations</i>, 2022.","ama":"Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning does not generalize strongly within the  same domain. In: <i>10th International Conference on Learning Representations</i>. ; 2022.","mla":"Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” <i>10th International Conference on Learning Representations</i>, 2022.","short":"L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022.","ista":"Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize strongly within the  same domain. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","apa":"Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge, M., … Brendel, W. (2022). Visual representation learning does not generalize strongly within the  same domain. In <i>10th International Conference on Learning Representations</i>. Virtual."},"status":"public","oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"Virtual","name":"ICLR: International Conference on Learning Representations","end_date":"2022-04-29","start_date":"2022-04-25"},"publication_status":"published","date_created":"2023-08-22T14:00:50Z","article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2107.08221","open_access":"1"}],"date_published":"2022-04-25T00:00:00Z","abstract":[{"text":"An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models\r\nthat learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards\r\nmore realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate\r\ngeneralization.","lang":"eng"}],"year":"2022","extern":"1","arxiv":1,"date_updated":"2023-09-11T09:40:52Z","month":"04"},{"intvolume":"        35","status":"public","author":[{"last_name":"Wenzel","full_name":"Wenzel, Florian","first_name":"Florian"},{"last_name":"Dittadi","first_name":"Andrea","full_name":"Dittadi, Andrea"},{"last_name":"Gehler","full_name":"Gehler, Peter Vincent","first_name":"Peter Vincent"},{"last_name":"Carl-Johann Simon-Gabriel","first_name":"Carl-Johann Simon-Gabriel","full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel"},{"last_name":"Horn","full_name":"Horn, Max","first_name":"Max"},{"full_name":"Zietlow, Dominik","first_name":"Dominik","last_name":"Zietlow"},{"last_name":"Kernert","first_name":"David","full_name":"Kernert, David"},{"last_name":"Russell","full_name":"Russell, Chris","first_name":"Chris"},{"last_name":"Brox","first_name":"Thomas","full_name":"Brox, Thomas"},{"first_name":"Bernt","full_name":"Schiele, Bernt","last_name":"Schiele"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"external_id":{"arxiv":["2207.09239"]},"language":[{"iso":"eng"}],"title":"Assaying out-of-distribution generalization in transfer learning","quality_controlled":"1","page":"7181-7198","volume":35,"date_created":"2023-08-22T14:01:13Z","conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2022-12-09","start_date":"2022-11-28","location":"New Orleans, LA, United States"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_identifier":{"isbn":["9781713871088"]},"oa":1,"type":"conference","department":[{"_id":"FrLo"}],"citation":{"ieee":"F. Wenzel <i>et al.</i>, “Assaying out-of-distribution generalization in transfer learning,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198.","chicago":"Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” In <i>36th Conference on Neural Information Processing Systems</i>, 35:7181–98. Neural Information Processing Systems Foundation, 2022.","short":"F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 7181–7198.","ama":"Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization in transfer learning. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.","mla":"Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98.","apa":"Wenzel, F., Dittadi, A., Gehler, P. V., Carl-Johann Simon-Gabriel, C.-J. S.-G., Horn, M., Zietlow, D., … Locatello, F. (2022). Assaying out-of-distribution generalization in transfer learning. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35, pp. 7181–7198). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","ista":"Wenzel F, Dittadi A, Gehler PV, Carl-Johann Simon-Gabriel C-JS-G, Horn M, Zietlow D, Kernert D, Russell C, Brox T, Schiele B, Schölkopf B, Locatello F. 2022. Assaying out-of-distribution generalization in transfer learning. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 7181–7198."},"publication":"36th Conference on Neural Information Processing Systems","_id":"14173","day":"15","year":"2022","extern":"1","abstract":[{"lang":"eng","text":"Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same\r\nexperimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and\r\nfew-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies."}],"scopus_import":"1","alternative_title":["Advances in Neural Information Processing Systems"],"month":"12","arxiv":1,"date_updated":"2023-09-06T10:34:43Z","publisher":"Neural Information Processing Systems Foundation","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2207.09239"}],"date_published":"2022-12-15T00:00:00Z","publication_status":"published","article_processing_charge":"No","oa_version":"Preprint"},{"publication_status":"published","article_processing_charge":"No","date_created":"2023-08-22T14:02:13Z","conference":{"start_date":"2022-04-25","name":"ICLR: International Conference on Learning Representations","end_date":"2022-04-29","location":"Virtual"},"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2022","abstract":[{"lang":"eng","text":"Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of\r\npretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents\r\nunder a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize."}],"extern":"1","month":"04","date_updated":"2023-09-11T09:48:36Z","arxiv":1,"main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2107.05686"}],"date_published":"2022-04-25T00:00:00Z","title":"The role of pretrained representations for the OOD generalization of  reinforcement learning agents","quality_controlled":"1","publication":"10th International Conference on Learning Representations","day":"25","_id":"14174","status":"public","author":[{"last_name":"Dittadi","full_name":"Dittadi, Andrea","first_name":"Andrea"},{"last_name":"Träuble","full_name":"Träuble, Frederik","first_name":"Frederik"},{"first_name":"Manuel","full_name":"Wüthrich, Manuel","last_name":"Wüthrich"},{"last_name":"Widmaier","first_name":"Felix","full_name":"Widmaier, Felix"},{"first_name":"Peter","full_name":"Gehler, Peter","last_name":"Gehler"},{"first_name":"Ole","full_name":"Winther, Ole","last_name":"Winther"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco"},{"last_name":"Bachem","full_name":"Bachem, Olivier","first_name":"Olivier"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"last_name":"Bauer","full_name":"Bauer, Stefan","first_name":"Stefan"}],"external_id":{"arxiv":["2107.05686"]},"language":[{"iso":"eng"}],"oa":1,"type":"conference","department":[{"_id":"FrLo"}],"citation":{"mla":"Dittadi, Andrea, et al. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” <i>10th International Conference on Learning Representations</i>, 2022.","ama":"Dittadi A, Träuble F, Wüthrich M, et al. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In: <i>10th International Conference on Learning Representations</i>. ; 2022.","short":"A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022.","ista":"Dittadi A, Träuble F, Wüthrich M, Widmaier F, Gehler P, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2022. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","apa":"Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., … Bauer, S. (2022). The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In <i>10th International Conference on Learning Representations</i>. Virtual.","ieee":"A. Dittadi <i>et al.</i>, “The role of pretrained representations for the OOD generalization of  reinforcement learning agents,” in <i>10th International Conference on Learning Representations</i>, Virtual, 2022.","chicago":"Dittadi, Andrea, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” In <i>10th International Conference on Learning Representations</i>, 2022."}},{"language":[{"iso":"eng"}],"oa":1,"author":[{"last_name":"Makansi","first_name":"Osama","full_name":"Makansi, Osama"},{"last_name":"Kügelgen","first_name":"Julius von","full_name":"Kügelgen, Julius von"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"full_name":"Janzing, Dominik","first_name":"Dominik","last_name":"Janzing"},{"last_name":"Brox","full_name":"Brox, Thomas","first_name":"Thomas"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"}],"external_id":{"arxiv":["2110.05304"]},"citation":{"chicago":"Makansi, Osama, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, and Bernhard Schölkopf. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” In <i>10th International Conference on Learning Representations</i>, 2022.","ieee":"O. Makansi <i>et al.</i>, “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in <i>10th International Conference on Learning Representations</i>, Virtual, 2022.","ista":"Makansi O, Kügelgen J von, Locatello F, Gehler P, Janzing D, Brox T, Schölkopf B. 2022. You mostly walk alone: Analyzing feature attribution in trajectory prediction. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","apa":"Makansi, O., Kügelgen, J. von, Locatello, F., Gehler, P., Janzing, D., Brox, T., &#38; Schölkopf, B. (2022). You mostly walk alone: Analyzing feature attribution in trajectory prediction. In <i>10th International Conference on Learning Representations</i>. Virtual.","short":"O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022.","ama":"Makansi O, Kügelgen J von, Locatello F, et al. You mostly walk alone: Analyzing feature attribution in trajectory prediction. In: <i>10th International Conference on Learning Representations</i>. ; 2022.","mla":"Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” <i>10th International Conference on Learning Representations</i>, 2022."},"department":[{"_id":"FrLo"}],"type":"conference","status":"public","_id":"14175","day":"25","publication":"10th International Conference on Learning Representations","quality_controlled":"1","title":"You mostly walk alone: Analyzing feature attribution in trajectory prediction","date_published":"2022-04-25T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2110.05304","open_access":"1"}],"extern":"1","year":"2022","abstract":[{"lang":"eng","text":"Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions\r\nof different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social\r\ninteraction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality."}],"date_updated":"2023-09-11T09:52:20Z","arxiv":1,"month":"04","oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"Virtual","start_date":"2022-04-25","name":"ICLR: International Conference on Learning Representations","end_date":"2022-04-29"},"publication_status":"published","date_created":"2023-08-22T14:02:34Z","article_processing_charge":"No"},{"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2211.02348","open_access":"1"}],"date_published":"2022-11-04T00:00:00Z","date_updated":"2023-09-13T09:35:59Z","arxiv":1,"month":"11","abstract":[{"text":"Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.","lang":"eng"}],"year":"2022","extern":"1","oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"New Orleans, LA, United States","end_date":"2022-12-09","name":"NeurIPS: Neural Information Processing Systems","start_date":"2022-11-28"},"article_processing_charge":"No","date_created":"2023-08-22T14:21:47Z","publication_status":"submitted","type":"conference","department":[{"_id":"FrLo"}],"citation":{"ama":"Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: <i>36th Conference on Neural Information Processing Systems</i>.","mla":"Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” <i>36th Conference on Neural Information Processing Systems</i>.","short":"N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d.","apa":"Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y., … Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems. In <i>36th Conference on Neural Information Processing Systems</i>. New Orleans, LA, United States.","ista":"Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li LE, Schölkopf B. A general purpose neural architecture for geospatial systems. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","ieee":"N. Rahaman <i>et al.</i>, “A general purpose neural architecture for geospatial systems,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States.","chicago":"Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General Purpose Neural Architecture for Geospatial Systems.” In <i>36th Conference on Neural Information Processing Systems</i>, n.d."},"language":[{"iso":"eng"}],"oa":1,"author":[{"last_name":"Rahaman","full_name":"Rahaman, Nasim","first_name":"Nasim"},{"last_name":"Weiss","full_name":"Weiss, Martin","first_name":"Martin"},{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"first_name":"Alexandre","full_name":"Lacoste, Alexandre","last_name":"Lacoste"},{"full_name":"Bengio, Yoshua","first_name":"Yoshua","last_name":"Bengio"},{"last_name":"Pal","first_name":"Chris","full_name":"Pal, Chris"},{"last_name":"Li","first_name":"Li Erran","full_name":"Li, Li Erran"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"}],"external_id":{"arxiv":["2211.02348"]},"status":"public","day":"04","_id":"14215","publication":"36th Conference on Neural Information Processing Systems","quality_controlled":"1","title":"A general purpose neural architecture for geospatial systems"},{"title":"Compositional multi-object reinforcement learning with linear relation networks","publication":"arXiv","day":"31","_id":"14220","doi":"10.48550/arXiv.2201.13388","status":"public","author":[{"full_name":"Mambelli, Davide","first_name":"Davide","last_name":"Mambelli"},{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"first_name":"Stefan","full_name":"Bauer, Stefan","last_name":"Bauer"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"},{"last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"external_id":{"arxiv":["2201.13388"]},"language":[{"iso":"eng"}],"oa":1,"type":"preprint","citation":{"ama":"Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2201.13388\">10.48550/arXiv.2201.13388</a>","mla":"Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” <i>ArXiv</i>, 2201.13388, doi:<a href=\"https://doi.org/10.48550/arXiv.2201.13388\">10.48550/arXiv.2201.13388</a>.","short":"D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).","apa":"Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., &#38; Locatello, F. (n.d.). Compositional multi-object reinforcement learning with linear relation networks. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2201.13388\">https://doi.org/10.48550/arXiv.2201.13388</a>","ista":"Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv, 2201.13388.","ieee":"D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional multi-object reinforcement learning with linear relation networks,” <i>arXiv</i>. .","chicago":"Mambelli, Davide, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, and Francesco Locatello. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2201.13388\">https://doi.org/10.48550/arXiv.2201.13388</a>."},"department":[{"_id":"FrLo"}],"publication_status":"submitted","date_created":"2023-08-22T14:23:16Z","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"2201.13388","oa_version":"Preprint","abstract":[{"lang":"eng","text":"Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as K2. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in K, allows agents to extrapolate and generalize zero-shot to any new object number."}],"year":"2022","extern":"1","month":"01","arxiv":1,"date_updated":"2024-10-14T12:27:39Z","date_published":"2022-01-31T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2201.13388","open_access":"1"}]},{"date_published":"2022-01-01T00:00:00Z","publisher":"Societe Mathematique de France","scopus_import":"1","abstract":[{"text":"Expander graphs (sparse but highly connected graphs) have, since their inception, been the source of deep links between Mathematics and Computer Science as well as applications to other areas. In recent years, a fascinating theory of high-dimensional expanders has begun to emerge, which is still in a formative stage but has nonetheless already lead to a number of striking results. Unlike for graphs, in higher dimensions there is a rich array of non-equivalent notions of expansion (coboundary expansion, cosystolic expansion, topological expansion, spectral expansion, etc.), with differents strengths and applications. In this talk, we will survey this landscape of high-dimensional expansion, with a focus on two main results. First, we will present Gromov’s Topological Overlap Theorem, which asserts that coboundary expansion (a quantitative version of vanishing mod 2 cohomology) implies topological expansion (roughly, the property that for every map from a simplicial complex to a manifold of the same dimension, the images of a positive fraction of the simplices have a point in common). Second, we will outline a construction of bounded degree 2-dimensional topological expanders, due to Kaufman, Kazhdan, and Lubotzky.","lang":"eng"}],"year":"2022","date_updated":"2025-09-10T09:55:10Z","month":"01","isi":1,"oa_version":"None","publication_status":"published","article_processing_charge":"No","type":"journal_article","department":[{"_id":"UlWa"}],"citation":{"chicago":"Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” <i>Bulletin de La Societe Mathematique de France</i>. Societe Mathematique de France, 2022. <a href=\"https://doi.org/10.24033/ast.1188\">https://doi.org/10.24033/ast.1188</a>.","ieee":"U. Wagner, “High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others),” <i>Bulletin de la Societe Mathematique de France</i>, vol. 438. Societe Mathematique de France, pp. 281–294, 2022.","ista":"Wagner U. 2022. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de la Societe Mathematique de France. 438, 281–294.","apa":"Wagner, U. (2022). High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). <i>Bulletin de La Societe Mathematique de France</i>. Societe Mathematique de France. <a href=\"https://doi.org/10.24033/ast.1188\">https://doi.org/10.24033/ast.1188</a>","short":"U. Wagner, Bulletin de La Societe Mathematique de France 438 (2022) 281–294.","ama":"Wagner U. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). <i>Bulletin de la Societe Mathematique de France</i>. 2022;438:281-294. doi:<a href=\"https://doi.org/10.24033/ast.1188\">10.24033/ast.1188</a>","mla":"Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” <i>Bulletin de La Societe Mathematique de France</i>, vol. 438, Societe Mathematique de France, 2022, pp. 281–94, doi:<a href=\"https://doi.org/10.24033/ast.1188\">10.24033/ast.1188</a>."},"doi":"10.24033/ast.1188","publication_identifier":{"eissn":["2102-622X"],"issn":["0037-9484"]},"day":"01","_id":"14381","publication":"Bulletin de la Societe Mathematique de France","volume":438,"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","corr_author":"1","article_type":"original","date_created":"2023-10-01T22:01:14Z","language":[{"iso":"eng"}],"external_id":{"isi":["000958364400007"]},"author":[{"first_name":"Uli","full_name":"Wagner, Uli","orcid":"0000-0002-1494-0568","last_name":"Wagner","id":"36690CA2-F248-11E8-B48F-1D18A9856A87"}],"intvolume":"       438","status":"public","page":"281-294","quality_controlled":"1","title":"High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others)"},{"external_id":{"pmid":["36543947"],"isi":["000934065100010"]},"author":[{"full_name":"Utzat, Hendrik","first_name":"Hendrik","last_name":"Utzat"},{"first_name":"Maria","full_name":"Ibáñez, Maria","last_name":"Ibáñez","orcid":"0000-0001-5013-2843","id":"43C61214-F248-11E8-B48F-1D18A9856A87"}],"language":[{"iso":"eng"}],"status":"public","intvolume":"       612","page":"638-639","title":"Molecular engineering enables bright blue LEDs","quality_controlled":"1","volume":612,"pmid":1,"keyword":["Multidisciplinary"],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2023-10-17T11:14:43Z","article_type":"letter_note","corr_author":"1","department":[{"_id":"MaIb"}],"type":"journal_article","citation":{"ista":"Utzat H, Ibáñez M. 2022. Molecular engineering enables bright blue LEDs. Nature. 612(7941), 638–639.","apa":"Utzat, H., &#38; Ibáñez, M. (2022). Molecular engineering enables bright blue LEDs. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/d41586-022-04447-0\">https://doi.org/10.1038/d41586-022-04447-0</a>","short":"H. Utzat, M. Ibáñez, Nature 612 (2022) 638–639.","mla":"Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” <i>Nature</i>, vol. 612, no. 7941, Springer Nature, 2022, pp. 638–39, doi:<a href=\"https://doi.org/10.1038/d41586-022-04447-0\">10.1038/d41586-022-04447-0</a>.","ama":"Utzat H, Ibáñez M. Molecular engineering enables bright blue LEDs. <i>Nature</i>. 2022;612(7941):638-639. doi:<a href=\"https://doi.org/10.1038/d41586-022-04447-0\">10.1038/d41586-022-04447-0</a>","chicago":"Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” <i>Nature</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/d41586-022-04447-0\">https://doi.org/10.1038/d41586-022-04447-0</a>.","ieee":"H. Utzat and M. Ibáñez, “Molecular engineering enables bright blue LEDs,” <i>Nature</i>, vol. 612, no. 7941. Springer Nature, pp. 638–639, 2022."},"publication_identifier":{"issn":["0028-0836"],"eissn":["1476-4687"]},"doi":"10.1038/d41586-022-04447-0","_id":"14437","day":"21","publication":"Nature","issue":"7941","publisher":"Springer Nature","date_published":"2022-12-21T00:00:00Z","month":"12","isi":1,"date_updated":"2025-09-10T09:55:51Z","year":"2022","scopus_import":"1","abstract":[{"text":"Future LEDs could be based on lead halide perovskites. A breakthrough in preparing device-compatible solids composed of nanoscale perovskite crystals overcomes a long-standing hurdle in making blue perovskite LEDs.","lang":"eng"}],"oa_version":"None","article_processing_charge":"No","publication_status":"published"},{"license":"https://creativecommons.org/licenses/by/4.0/","file_date_updated":"2024-10-14T18:11:45Z","title":"Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"day":"01","_id":"18291","doi":"10.15479/AT:ISTA:18291","status":"public","author":[{"id":"38DB5788-F248-11E8-B48F-1D18A9856A87","last_name":"Katsaros","orcid":"0000-0001-8342-202X","full_name":"Katsaros, Georgios","first_name":"Georgios"},{"id":"4C473F58-F248-11E8-B48F-1D18A9856A87","last_name":"Jirovec","orcid":"0000-0002-7197-4801","first_name":"Daniel","full_name":"Jirovec, Daniel"}],"oa":1,"department":[{"_id":"GeKa"}],"type":"research_data","citation":{"ieee":"G. Katsaros and D. Jirovec, “Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences.” Institute of Science and Technology Austria, 2022.","chicago":"Katsaros, Georgios, and Daniel Jirovec. “Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences.” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/AT:ISTA:18291\">https://doi.org/10.15479/AT:ISTA:18291</a>.","short":"G. Katsaros, D. Jirovec, (2022).","ama":"Katsaros G, Jirovec D. Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences. 2022. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:18291\">10.15479/AT:ISTA:18291</a>","mla":"Katsaros, Georgios, and Daniel Jirovec. <i>Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences</i>. Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:18291\">10.15479/AT:ISTA:18291</a>.","apa":"Katsaros, G., &#38; Jirovec, D. (2022). Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:18291\">https://doi.org/10.15479/AT:ISTA:18291</a>","ista":"Katsaros G, Jirovec D. 2022. Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:18291\">10.15479/AT:ISTA:18291</a>."},"corr_author":"1","date_created":"2024-10-09T19:35:03Z","article_processing_charge":"No","file":[{"content_type":"application/x-zip-compressed","checksum":"3128dffbd09267b93c2d0b1425fd3ba2","file_name":"SOIPaper.zip","file_size":25566516,"date_updated":"2024-10-09T19:31:35Z","success":1,"file_id":"18292","creator":"gkatsaro","access_level":"open_access","relation":"main_file","date_created":"2024-10-09T19:31:35Z"},{"file_name":"Readme.txt","checksum":"df077d2f4652afeb3bf100068e88aa48","content_type":"text/plain","success":1,"date_updated":"2024-10-14T18:11:45Z","file_size":6776,"file_id":"18442","date_created":"2024-10-14T18:11:45Z","relation":"main_file","access_level":"open_access","creator":"gkatsaro"}],"user_id":"68b8ca59-c5b3-11ee-8790-cd641c68093d","oa_version":"None","year":"2022","related_material":{"record":[{"id":"10920","status":"public","relation":"research_paper"}]},"month":"03","date_updated":"2025-04-15T07:15:24Z","publisher":"Institute of Science and Technology Austria","date_published":"2022-03-01T00:00:00Z","has_accepted_license":"1"},{"doi":"10.1016/j.sctalk.2022.100038","acknowledgement":"This talk presents parts of my PhD work, conducted at IUSTI in Marseille under the supervision of Yoël Forterre and Bloen Metzger. It aslo benefited from contributions from Antoine Bérut, and some of the data was acquired by Pauline Dame as part of a summer internship.\r\nThis work was supported by the European Research Council (ERC) under the European Union Horizon 2020 Research and Innovation program (ERC Grant 647384) and by the Labex MEC (ANR-10-LABX-0092) under the 647384) and by the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the French government program Investissements d'Avenir, and by ANR ScienceFriction (No. ANR-18-CE30-0024).","publication_identifier":{"eissn":["2772-5693"]},"type":"journal_article","citation":{"chicago":"Clavaud, Cécile. “Shear Thickening in Dense Suspensions: An Experimental Study.” <i>Science Talks</i>. Elsevier, 2022. <a href=\"https://doi.org/10.1016/j.sctalk.2022.100038\">https://doi.org/10.1016/j.sctalk.2022.100038</a>.","ieee":"C. Clavaud, “Shear thickening in dense suspensions: an experimental study,” <i>Science Talks</i>, vol. 3. Elsevier, 2022.","apa":"Clavaud, C. (2022). Shear thickening in dense suspensions: an experimental study. <i>Science Talks</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.sctalk.2022.100038\">https://doi.org/10.1016/j.sctalk.2022.100038</a>","ista":"Clavaud C. 2022. Shear thickening in dense suspensions: an experimental study. Science Talks. 3, 100038.","short":"C. Clavaud, Science Talks 3 (2022).","ama":"Clavaud C. Shear thickening in dense suspensions: an experimental study. <i>Science Talks</i>. 2022;3. doi:<a href=\"https://doi.org/10.1016/j.sctalk.2022.100038\">10.1016/j.sctalk.2022.100038</a>","mla":"Clavaud, Cécile. “Shear Thickening in Dense Suspensions: An Experimental Study.” <i>Science Talks</i>, vol. 3, 100038, Elsevier, 2022, doi:<a href=\"https://doi.org/10.1016/j.sctalk.2022.100038\">10.1016/j.sctalk.2022.100038</a>."},"department":[{"_id":"ScWa"}],"oa":1,"publication":"Science Talks","file_date_updated":"2024-12-11T09:22:19Z","day":"01","_id":"18606","date_updated":"2024-12-11T09:24:57Z","month":"08","abstract":[{"text":"Shear thickening is an intriguing rheological behaviour which consists in a brutal increase in the viscosity above a critical shear rate. It is famously encountered in suspensions of corn starch in water. Despite having been discovered in the early 1930's, its underlying mechanisms remained a mystery for a long time. In 2013–14, numerical and theoretical works [[1], [2], [3]] put forward a frictional transition scenario to explain this phenomenon.\r\nIn this talk, I will present experimental work investigating this frictional transition scenario. In order to test the ideas of this model, one has to go further than standard rheological techniques, since they do not provide access to the frictional state of the measured suspension. I will thus focus on the techniques that we developed in order to evidence the frictional transition and link it to the presence of a shear-thickening behaviour.","lang":"eng"}],"scopus_import":"1","year":"2022","date_published":"2022-08-01T00:00:00Z","publisher":"Elsevier","article_processing_charge":"No","publication_status":"published","OA_place":"publisher","oa_version":"Published Version","file":[{"content_type":"application/pdf","checksum":"379a5f0b2684cd5393a23be374591484","file_name":"2022_ScienceTalks_Clavaud.pdf","file_size":1128564,"date_updated":"2024-12-03T08:41:48Z","success":1,"file_id":"18607","creator":"dernst","access_level":"open_access","relation":"main_file","date_created":"2024-12-03T08:41:48Z"},{"creator":"dernst","access_level":"open_access","relation":"main_file","date_created":"2024-12-11T09:22:13Z","file_id":"18646","file_size":93265727,"date_updated":"2024-12-11T09:22:13Z","success":1,"content_type":"video/mp4","checksum":"666c0bd9af8432437554d0c75c540809","file_name":"2024_ScienceTalk_Clavaud_Video.mp4"},{"date_updated":"2024-12-11T09:22:19Z","file_size":58282147,"file_name":"2024_ScienceTalk__Clavaud_QA.mp4","checksum":"8fd0d6224d7a0125fcf7d9ca0d80d700","content_type":"video/mp4","date_created":"2024-12-11T09:22:19Z","relation":"supplementary_material","access_level":"open_access","creator":"dernst","file_id":"18647"}],"status":"public","intvolume":"         3","language":[{"iso":"eng"}],"author":[{"last_name":"Clavaud","orcid":"0000-0002-1843-3803","first_name":"Cécile","full_name":"Clavaud, Cécile","id":"5f654c5d-04a1-11eb-ab36-ba9ffec58bd8"}],"quality_controlled":"1","title":"Shear thickening in dense suspensions: an experimental study","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"DOAJ_listed":"1","volume":3,"OA_type":"gold","has_accepted_license":"1","date_created":"2024-12-01T23:01:55Z","ddc":["530"],"corr_author":"1","article_type":"original","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"100038"},{"date_published":"2022-09-01T00:00:00Z","publisher":"Embo Press","scopus_import":"1","abstract":[{"text":"Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.","lang":"eng"}],"year":"2022","date_updated":"2025-06-11T14:10:18Z","month":"09","isi":1,"oa_version":"Published Version","file":[{"file_id":"12446","access_level":"open_access","creator":"dernst","date_created":"2023-01-30T09:49:55Z","relation":"main_file","content_type":"application/pdf","file_name":"2022_MolecularSystemsBio_Angermayr.pdf","checksum":"8b1d8f5ea20c8408acf466435fb6ae01","file_size":1098812,"date_updated":"2023-01-30T09:49:55Z","success":1}],"publication_status":"published","article_processing_charge":"No","oa":1,"citation":{"short":"A. Angermayr, T.Y. Pang, G. Chevereau, K. Mitosch, M.J. Lercher, M.T. Bollenbach, Molecular Systems Biology 18 (2022).","mla":"Angermayr, Andreas, et al. “Growth‐mediated Negative Feedback Shapes Quantitative Antibiotic Response.” <i>Molecular Systems Biology</i>, vol. 18, no. 9, e10490, Embo Press, 2022, doi:<a href=\"https://doi.org/10.15252/msb.202110490\">10.15252/msb.202110490</a>.","ama":"Angermayr A, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach MT. Growth‐mediated negative feedback shapes quantitative antibiotic response. <i>Molecular Systems Biology</i>. 2022;18(9). doi:<a href=\"https://doi.org/10.15252/msb.202110490\">10.15252/msb.202110490</a>","apa":"Angermayr, A., Pang, T. Y., Chevereau, G., Mitosch, K., Lercher, M. J., &#38; Bollenbach, M. T. (2022). Growth‐mediated negative feedback shapes quantitative antibiotic response. <i>Molecular Systems Biology</i>. Embo Press. <a href=\"https://doi.org/10.15252/msb.202110490\">https://doi.org/10.15252/msb.202110490</a>","ista":"Angermayr A, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach MT. 2022. Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. 18(9), e10490.","ieee":"A. Angermayr, T. Y. Pang, G. Chevereau, K. Mitosch, M. J. Lercher, and M. T. Bollenbach, “Growth‐mediated negative feedback shapes quantitative antibiotic response,” <i>Molecular Systems Biology</i>, vol. 18, no. 9. Embo Press, 2022.","chicago":"Angermayr, Andreas, Tin Yau Pang, Guillaume Chevereau, Karin Mitosch, Martin J Lercher, and Mark Tobias Bollenbach. “Growth‐mediated Negative Feedback Shapes Quantitative Antibiotic Response.” <i>Molecular Systems Biology</i>. Embo Press, 2022. <a href=\"https://doi.org/10.15252/msb.202110490\">https://doi.org/10.15252/msb.202110490</a>."},"department":[{"_id":"ToBo"}],"type":"journal_article","doi":"10.15252/msb.202110490","publication_identifier":{"eissn":["1744-4292"]},"acknowledgement":"This work was in part supported by Human Frontier Science Program GrantRGP0042/2013, Marie Curie Career Integration Grant303507, AustrianScience Fund (FWF) Grant P27201-B22, and German Research Foundation(DFG) Collaborative Research Center (SFB)1310to TB. SAA was supportedby the European Union’s Horizon2020Research and Innovation Programunder the Marie Skłodowska-Curie Grant agreement No707352. We wouldlike to thank the Bollenbach group for regular fruitful discussions. We areparticularly thankful for the technical assistance of Booshini Fernando andfor discussions of the theoretical aspects with Gerrit Ansmann. We areindebted to Bor Kavˇciˇc for invaluable advice, help with setting up theluciferase-based growth monitoring system, and for sharing plasmids. Weacknowledge the IST Austria Miba Machine Shop for their support inbuilding a housing for the stacker of the plate reader, which enabled thehigh-throughput luciferase-based experiments. We are grateful to RosalindAllen, Bor Kavˇciˇc and Dor Russ for feedback on the manuscript. Open Accessfunding enabled and organized by Projekt DEAL.","_id":"12261","day":"01","acknowledged_ssus":[{"_id":"M-Shop"}],"file_date_updated":"2023-01-30T09:49:55Z","issue":"9","publication":"Molecular Systems Biology","has_accepted_license":"1","volume":18,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"e10490","keyword":["Applied Mathematics","Computational Theory and Mathematics","General Agricultural and Biological Sciences","General Immunology and Microbiology","General Biochemistry","Genetics and Molecular Biology","Information Systems"],"pmid":1,"article_type":"original","date_created":"2023-01-16T09:58:34Z","ddc":["570"],"language":[{"iso":"eng"}],"external_id":{"pmid":["36124745"],"isi":["000856482800001"]},"author":[{"id":"4677C796-F248-11E8-B48F-1D18A9856A87","last_name":"Angermayr","orcid":"0000-0001-8619-2223","first_name":"Andreas","full_name":"Angermayr, Andreas"},{"first_name":"Tin Yau","full_name":"Pang, Tin Yau","last_name":"Pang"},{"first_name":"Guillaume","full_name":"Chevereau, Guillaume","last_name":"Chevereau"},{"id":"39B66846-F248-11E8-B48F-1D18A9856A87","last_name":"Mitosch","full_name":"Mitosch, Karin","first_name":"Karin"},{"first_name":"Martin J","full_name":"Lercher, Martin J","last_name":"Lercher"},{"orcid":"0000-0003-4398-476X","last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"intvolume":"        18","status":"public","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"quality_controlled":"1","title":"Growth‐mediated negative feedback shapes quantitative antibiotic response"},{"volume":35,"related_material":{"record":[{"id":"12265","relation":"other","status":"public"}]},"has_accepted_license":"1","date_created":"2023-01-16T09:59:24Z","ddc":["570"],"corr_author":"1","article_type":"review","pmid":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","keyword":["Ecology","Evolution","Behavior and Systematics"],"status":"public","intvolume":"        35","language":[{"iso":"eng"}],"external_id":{"pmid":["36063156"],"isi":["000849851100002"]},"author":[{"last_name":"Westram","orcid":"0000-0003-1050-4969","first_name":"Anja M","full_name":"Westram, Anja M","id":"3C147470-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Sean","full_name":"Stankowski, Sean","last_name":"Stankowski","id":"43161670-5719-11EA-8025-FABC3DDC885E"},{"id":"455235B8-F248-11E8-B48F-1D18A9856A87","full_name":"Surendranadh, Parvathy","first_name":"Parvathy","orcid":"0000-0001-6395-386X","last_name":"Surendranadh"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","orcid":"0000-0002-8548-5240","first_name":"Nicholas H","full_name":"Barton, Nicholas H"}],"quality_controlled":"1","title":"What is reproductive isolation?","page":"1143-1164","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2025-04-15T08:20:40Z","isi":1,"month":"09","scopus_import":"1","abstract":[{"text":"Reproductive isolation (RI) is a core concept in evolutionary biology. It has been the central focus of speciation research since the modern synthesis and is the basis by which biological species are defined. Despite this, the term is used in seemingly different ways, and attempts to quantify RI have used very different approaches. After showing that the field lacks a clear definition of the term, we attempt to clarify key issues, including what RI is, how it can be quantified in principle, and how it can be measured in practice. Following other definitions with a genetic focus, we propose that RI is a quantitative measure of the effect that genetic differences between populations have on gene flow. Specifically, RI compares the flow of neutral alleles in the presence of these genetic differences to the flow without any such differences. RI is thus greater than zero when genetic differences between populations reduce the flow of neutral alleles between populations. We show how RI can be quantified in a range of scenarios. A key conclusion is that RI depends strongly on circumstances—including the spatial, temporal and genomic context—making it difficult to compare across systems. After reviewing methods for estimating RI from data, we conclude that it is difficult to measure in practice. We discuss our findings in light of the goals of speciation research and encourage the use of methods for estimating RI that integrate organismal and genetic approaches.","lang":"eng"}],"year":"2022","date_published":"2022-09-01T00:00:00Z","publisher":"Wiley","project":[{"grant_number":"P32166","_id":"05959E1C-7A3F-11EA-A408-12923DDC885E","name":"Snapdragon Speciation"}],"article_processing_charge":"Yes (via OA deal)","publication_status":"published","file":[{"content_type":"application/pdf","file_name":"2022_JourEvoBiology_Westram.pdf","checksum":"f08de57112330a7ee88d2e1b20576a1e","file_size":3146793,"success":1,"date_updated":"2023-01-30T10:05:31Z","file_id":"12448","access_level":"open_access","creator":"dernst","date_created":"2023-01-30T10:05:31Z","relation":"main_file"}],"oa_version":"Published Version","doi":"10.1111/jeb.14005","acknowledgement":"We are grateful to the participants of the ESEB satellite symposium ‘Understanding reproductive isolation: bridging conceptual barriers in  speciation  research’  in  2021  for  the  interesting  discussions  that  helped  us  clarify  the  thoughts  presented  in  this  article.  We  thank  Roger Butlin, Michael Turelli and two anonymous reviewers for their thoughtful comments on this manuscript. We are also very grateful to Roger Butlin and the Barton Group for the continued conversa-tions about RI. In addition, we thank all participants of the speciation survey. Part of this work was funded by the Austrian Science Fund FWF (grant P 32166)","publication_identifier":{"issn":["1010-061X"],"eissn":["1420-9101"]},"type":"journal_article","department":[{"_id":"NiBa"}],"citation":{"ista":"Westram AM, Stankowski S, Surendranadh P, Barton NH. 2022. What is reproductive isolation? Journal of Evolutionary Biology. 35(9), 1143–1164.","apa":"Westram, A. M., Stankowski, S., Surendranadh, P., &#38; Barton, N. H. (2022). What is reproductive isolation? <i>Journal of Evolutionary Biology</i>. Wiley. <a href=\"https://doi.org/10.1111/jeb.14005\">https://doi.org/10.1111/jeb.14005</a>","short":"A.M. Westram, S. Stankowski, P. Surendranadh, N.H. Barton, Journal of Evolutionary Biology 35 (2022) 1143–1164.","ama":"Westram AM, Stankowski S, Surendranadh P, Barton NH. What is reproductive isolation? <i>Journal of Evolutionary Biology</i>. 2022;35(9):1143-1164. doi:<a href=\"https://doi.org/10.1111/jeb.14005\">10.1111/jeb.14005</a>","mla":"Westram, Anja M., et al. “What Is Reproductive Isolation?” <i>Journal of Evolutionary Biology</i>, vol. 35, no. 9, Wiley, 2022, pp. 1143–64, doi:<a href=\"https://doi.org/10.1111/jeb.14005\">10.1111/jeb.14005</a>.","chicago":"Westram, Anja M, Sean Stankowski, Parvathy Surendranadh, and Nicholas H Barton. “What Is Reproductive Isolation?” <i>Journal of Evolutionary Biology</i>. Wiley, 2022. <a href=\"https://doi.org/10.1111/jeb.14005\">https://doi.org/10.1111/jeb.14005</a>.","ieee":"A. M. Westram, S. Stankowski, P. Surendranadh, and N. H. Barton, “What is reproductive isolation?,” <i>Journal of Evolutionary Biology</i>, vol. 35, no. 9. Wiley, pp. 1143–1164, 2022."},"oa":1,"publication":"Journal of Evolutionary Biology","file_date_updated":"2023-01-30T10:05:31Z","issue":"9","day":"01","_id":"12264"},{"acknowledgement":"We  are  very  grateful  to  the  authors  of  the  commentaries  for  the  interesting discussion and to Luke Holman for handling this set of manuscripts. Part of this work was funded by the Austrian Science Fund FWF (grant P 32166).","publication_identifier":{"eissn":["1420-9101"],"issn":["1010-061X"]},"doi":"10.1111/jeb.14082","type":"journal_article","citation":{"ieee":"A. M. Westram, S. Stankowski, P. Surendranadh, and N. H. Barton, “Reproductive isolation, speciation, and the value of disagreement: A reply to the commentaries on ‘What is reproductive isolation?,’” <i>Journal of Evolutionary Biology</i>, vol. 35, no. 9. Wiley, pp. 1200–1205, 2022.","chicago":"Westram, Anja M, Sean Stankowski, Parvathy Surendranadh, and Nicholas H Barton. “Reproductive Isolation, Speciation, and the Value of Disagreement: A Reply to the Commentaries on ‘What Is Reproductive Isolation?’” <i>Journal of Evolutionary Biology</i>. Wiley, 2022. <a href=\"https://doi.org/10.1111/jeb.14082\">https://doi.org/10.1111/jeb.14082</a>.","short":"A.M. Westram, S. Stankowski, P. Surendranadh, N.H. Barton, Journal of Evolutionary Biology 35 (2022) 1200–1205.","ama":"Westram AM, Stankowski S, Surendranadh P, Barton NH. Reproductive isolation, speciation, and the value of disagreement: A reply to the commentaries on ‘What is reproductive isolation?’ <i>Journal of Evolutionary Biology</i>. 2022;35(9):1200-1205. doi:<a href=\"https://doi.org/10.1111/jeb.14082\">10.1111/jeb.14082</a>","mla":"Westram, Anja M., et al. “Reproductive Isolation, Speciation, and the Value of Disagreement: A Reply to the Commentaries on ‘What Is Reproductive Isolation?’” <i>Journal of Evolutionary Biology</i>, vol. 35, no. 9, Wiley, 2022, pp. 1200–05, doi:<a href=\"https://doi.org/10.1111/jeb.14082\">10.1111/jeb.14082</a>.","apa":"Westram, A. M., Stankowski, S., Surendranadh, P., &#38; Barton, N. H. (2022). Reproductive isolation, speciation, and the value of disagreement: A reply to the commentaries on ‘What is reproductive isolation?’ <i>Journal of Evolutionary Biology</i>. Wiley. <a href=\"https://doi.org/10.1111/jeb.14082\">https://doi.org/10.1111/jeb.14082</a>","ista":"Westram AM, Stankowski S, Surendranadh P, Barton NH. 2022. Reproductive isolation, speciation, and the value of disagreement: A reply to the commentaries on ‘What is reproductive isolation?’ Journal of Evolutionary Biology. 35(9), 1200–1205."},"department":[{"_id":"NiBa"}],"oa":1,"publication":"Journal of Evolutionary Biology","issue":"9","file_date_updated":"2023-01-30T10:14:09Z","_id":"12265","day":"01","month":"09","isi":1,"date_updated":"2025-04-15T08:20:40Z","scopus_import":"1","year":"2022","publisher":"Wiley","date_published":"2022-09-01T00:00:00Z","article_processing_charge":"Yes (via OA deal)","project":[{"_id":"05959E1C-7A3F-11EA-A408-12923DDC885E","name":"Snapdragon Speciation","grant_number":"P32166"}],"publication_status":"published","oa_version":"Published Version","file":[{"content_type":"application/pdf","file_name":"2022_JourEvoBiology_Westram_Response.pdf","checksum":"27268009e5eec030bc10667a4ac5ed4c","file_size":349603,"date_updated":"2023-01-30T10:14:09Z","success":1,"file_id":"12449","access_level":"open_access","creator":"dernst","date_created":"2023-01-30T10:14:09Z","relation":"main_file"}],"status":"public","intvolume":"        35","author":[{"id":"3C147470-F248-11E8-B48F-1D18A9856A87","full_name":"Westram, Anja M","first_name":"Anja M","last_name":"Westram","orcid":"0000-0003-1050-4969"},{"id":"43161670-5719-11EA-8025-FABC3DDC885E","last_name":"Stankowski","first_name":"Sean","full_name":"Stankowski, Sean"},{"id":"455235B8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6395-386X","last_name":"Surendranadh","first_name":"Parvathy","full_name":"Surendranadh, Parvathy"},{"orcid":"0000-0002-8548-5240","last_name":"Barton","full_name":"Barton, Nicholas H","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}],"external_id":{"isi":["000849851100009"]},"language":[{"iso":"eng"}],"title":"Reproductive isolation, speciation, and the value of disagreement: A reply to the commentaries on ‘What is reproductive isolation?’","quality_controlled":"1","page":"1200-1205","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"related_material":{"record":[{"id":"12264","relation":"other","status":"public"}]},"volume":35,"has_accepted_license":"1","ddc":["570"],"date_created":"2023-01-16T09:59:37Z","article_type":"letter_note","corr_author":"1","keyword":["Ecology","Evolution","Behavior and Systematics"],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8"},{"date_created":"2023-01-16T10:00:28Z","ddc":["570"],"article_type":"original","pmid":1,"article_number":"983507","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","keyword":["Cancer Research","Oncology"],"volume":12,"has_accepted_license":"1","quality_controlled":"1","title":"Substrate stiffness effect on molecular crosstalk of epithelial-mesenchymal transition mediators of human glioblastoma cells","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"status":"public","intvolume":"        12","language":[{"iso":"eng"}],"external_id":{"pmid":["36091138"],"isi":["000856524900001"]},"author":[{"id":"36035796-5ACA-11E9-A75E-7AF2E5697425","orcid":"0000-0003-1843-3173","last_name":"Basilico","first_name":"Bernadette","full_name":"Basilico, Bernadette"},{"first_name":"Ilaria Elena","full_name":"Palamà, Ilaria Elena","last_name":"Palamà"},{"last_name":"D’Amone","first_name":"Stefania","full_name":"D’Amone, Stefania"},{"first_name":"Clotilde","full_name":"Lauro, Clotilde","last_name":"Lauro"},{"last_name":"Rosito","first_name":"Maria","full_name":"Rosito, Maria"},{"first_name":"Maddalena","full_name":"Grieco, Maddalena","last_name":"Grieco"},{"last_name":"Ratano","first_name":"Patrizia","full_name":"Ratano, Patrizia"},{"full_name":"Cordella, Federica","first_name":"Federica","last_name":"Cordella"},{"full_name":"Sanchini, Caterina","first_name":"Caterina","last_name":"Sanchini"},{"last_name":"Di Angelantonio","full_name":"Di Angelantonio, Silvia","first_name":"Silvia"},{"full_name":"Ragozzino, Davide","first_name":"Davide","last_name":"Ragozzino"},{"full_name":"Cascione, Mariafrancesca","first_name":"Mariafrancesca","last_name":"Cascione"},{"full_name":"Gigli, Giuseppe","first_name":"Giuseppe","last_name":"Gigli"},{"first_name":"Barbara","full_name":"Cortese, Barbara","last_name":"Cortese"}],"article_processing_charge":"No","publication_status":"published","oa_version":"Published Version","file":[{"file_id":"12450","date_created":"2023-01-30T10:25:21Z","relation":"main_file","access_level":"open_access","creator":"dernst","file_name":"2022_FrontiersOntology_Basilico.pdf","checksum":"efc7edf9f626af31853790c5b598a68c","content_type":"application/pdf","success":1,"date_updated":"2023-01-30T10:25:21Z","file_size":13588502}],"date_updated":"2023-08-04T09:54:16Z","month":"08","isi":1,"scopus_import":"1","year":"2022","abstract":[{"text":"The complexity of the microenvironment effects on cell response, show accumulating evidence that glioblastoma (GBM) migration and invasiveness are influenced by the mechanical rigidity of their surroundings. The epithelial–mesenchymal transition (EMT) is a well-recognized driving force of the invasive behavior of cancer. However, the primary mechanisms of EMT initiation and progression remain unclear. We have previously showed that certain substrate stiffness can selectively stimulate human GBM U251-MG and GL15 glioblastoma cell lines motility. The present study unifies several known EMT mediators to uncover the reason of the regulation and response to these stiffnesses. Our results revealed that changing the rigidity of the mechanical environment tuned the response of both cell lines through change in morphological features, epithelial-mesenchymal markers (E-, N-Cadherin), EGFR and ROS expressions in an interrelated manner. Specifically, a stiffer microenvironment induced a mesenchymal cell shape, a more fragmented morphology, higher intracellular cytosolic ROS expression and lower mitochondrial ROS. Finally, we observed that cells more motile showed a more depolarized mitochondrial membrane potential. Unravelling the process that regulates GBM cells’ infiltrative behavior could provide new opportunities for identification of new targets and less invasive approaches for treatment.","lang":"eng"}],"date_published":"2022-08-25T00:00:00Z","publisher":"Frontiers Media","publication":"Frontiers in Oncology","file_date_updated":"2023-01-30T10:25:21Z","_id":"12268","day":"25","doi":"10.3389/fonc.2022.983507","publication_identifier":{"issn":["2234-943X"]},"acknowledgement":"The research leading to these results has received funding from AIRC under IG 2021 - ID. 26328 project – P.I. Cortese Barbara and AIRC under MFAG 2015 - ID. 16803 project – “P.I. Cortese Barbara”. The authors are also grateful to the ”Tecnopolo per la medicina di precisione” (TecnoMed Puglia) - Regione Puglia: DGR n.2117 del 21/11/2018, CUP: B84I18000540002 and “Tecnopolo di Nanotecnologia e Fotonica per la medicina di precisione” (TECNOMED) - FISR/MIUR-CNR: delibera CIPE n.3449 del 7-08-2017, CUP: B83B17000010001.\r\nWe thank Dr. Francesca Pagani for useful technical support. We thank also Irene Iacuitto, Giovanna Loffredo and Manuela Marchetti for practical administrative support.","type":"journal_article","citation":{"ieee":"B. Basilico <i>et al.</i>, “Substrate stiffness effect on molecular crosstalk of epithelial-mesenchymal transition mediators of human glioblastoma cells,” <i>Frontiers in Oncology</i>, vol. 12. Frontiers Media, 2022.","chicago":"Basilico, Bernadette, Ilaria Elena Palamà, Stefania D’Amone, Clotilde Lauro, Maria Rosito, Maddalena Grieco, Patrizia Ratano, et al. “Substrate Stiffness Effect on Molecular Crosstalk of Epithelial-Mesenchymal Transition Mediators of Human Glioblastoma Cells.” <i>Frontiers in Oncology</i>. Frontiers Media, 2022. <a href=\"https://doi.org/10.3389/fonc.2022.983507\">https://doi.org/10.3389/fonc.2022.983507</a>.","mla":"Basilico, Bernadette, et al. “Substrate Stiffness Effect on Molecular Crosstalk of Epithelial-Mesenchymal Transition Mediators of Human Glioblastoma Cells.” <i>Frontiers in Oncology</i>, vol. 12, 983507, Frontiers Media, 2022, doi:<a href=\"https://doi.org/10.3389/fonc.2022.983507\">10.3389/fonc.2022.983507</a>.","ama":"Basilico B, Palamà IE, D’Amone S, et al. Substrate stiffness effect on molecular crosstalk of epithelial-mesenchymal transition mediators of human glioblastoma cells. <i>Frontiers in Oncology</i>. 2022;12. doi:<a href=\"https://doi.org/10.3389/fonc.2022.983507\">10.3389/fonc.2022.983507</a>","short":"B. Basilico, I.E. Palamà, S. D’Amone, C. Lauro, M. Rosito, M. Grieco, P. Ratano, F. Cordella, C. Sanchini, S. Di Angelantonio, D. Ragozzino, M. Cascione, G. Gigli, B. Cortese, Frontiers in Oncology 12 (2022).","apa":"Basilico, B., Palamà, I. E., D’Amone, S., Lauro, C., Rosito, M., Grieco, M., … Cortese, B. (2022). Substrate stiffness effect on molecular crosstalk of epithelial-mesenchymal transition mediators of human glioblastoma cells. <i>Frontiers in Oncology</i>. Frontiers Media. <a href=\"https://doi.org/10.3389/fonc.2022.983507\">https://doi.org/10.3389/fonc.2022.983507</a>","ista":"Basilico B, Palamà IE, D’Amone S, Lauro C, Rosito M, Grieco M, Ratano P, Cordella F, Sanchini C, Di Angelantonio S, Ragozzino D, Cascione M, Gigli G, Cortese B. 2022. Substrate stiffness effect on molecular crosstalk of epithelial-mesenchymal transition mediators of human glioblastoma cells. Frontiers in Oncology. 12, 983507."},"department":[{"_id":"GaNo"}],"oa":1},{"publisher":"American Physical Society","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2106.08373","open_access":"1"}],"date_published":"2022-08-31T00:00:00Z","scopus_import":"1","abstract":[{"lang":"eng","text":"We study the thermalization of a small XX chain coupled to long, gapped XXZ leads at either side by observing the relaxation dynamics of the whole system. Using extensive tensor network simulations, we show that such systems, although not integrable, appear to show either extremely slow thermalization or even lack thereof since the two cannot be distinguished within the accuracy of our numerics. We show that the persistent oscillations observed in the spin current in the middle of the XX chain are related to eigenstates of the entire system located within the gap of the boundary chains. We find from exact diagonalization that some of these states remain strictly localized within the XX chain and do not hybridize with the rest of the system. The frequencies of the persistent oscillations determined by numerical simulations of dynamics match the energy differences between these states exactly. This has important implications for open systems, where the strongly interacting leads are often assumed to thermalize the central system. Our results suggest that, if we employ gapped systems for the leads, this assumption does not hold."}],"year":"2022","isi":1,"month":"08","arxiv":1,"date_updated":"2025-04-14T07:52:06Z","oa_version":"Preprint","publication_status":"published","project":[{"_id":"23841C26-32DE-11EA-91FC-C7463DDC885E","name":"Non-Ergodic Quantum Matter: Universality, Dynamics and Control","grant_number":"850899","call_identifier":"H2020"}],"article_processing_charge":"No","oa":1,"department":[{"_id":"MaSe"}],"type":"journal_article","citation":{"apa":"Ljubotina, M., Roy, D., &#38; Prosen, T. (2022). Absence of thermalization of free systems coupled to gapped interacting reservoirs. <i>Physical Review B</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevb.106.054314\">https://doi.org/10.1103/physrevb.106.054314</a>","ista":"Ljubotina M, Roy D, Prosen T. 2022. Absence of thermalization of free systems coupled to gapped interacting reservoirs. Physical Review B. 106(5), 054314.","ama":"Ljubotina M, Roy D, Prosen T. Absence of thermalization of free systems coupled to gapped interacting reservoirs. <i>Physical Review B</i>. 2022;106(5). doi:<a href=\"https://doi.org/10.1103/physrevb.106.054314\">10.1103/physrevb.106.054314</a>","mla":"Ljubotina, Marko, et al. “Absence of Thermalization of Free Systems Coupled to Gapped Interacting Reservoirs.” <i>Physical Review B</i>, vol. 106, no. 5, 054314, American Physical Society, 2022, doi:<a href=\"https://doi.org/10.1103/physrevb.106.054314\">10.1103/physrevb.106.054314</a>.","short":"M. Ljubotina, D. Roy, T. Prosen, Physical Review B 106 (2022).","chicago":"Ljubotina, Marko, Dibyendu Roy, and Tomaž Prosen. “Absence of Thermalization of Free Systems Coupled to Gapped Interacting Reservoirs.” <i>Physical Review B</i>. American Physical Society, 2022. <a href=\"https://doi.org/10.1103/physrevb.106.054314\">https://doi.org/10.1103/physrevb.106.054314</a>.","ieee":"M. Ljubotina, D. Roy, and T. Prosen, “Absence of thermalization of free systems coupled to gapped interacting reservoirs,” <i>Physical Review B</i>, vol. 106, no. 5. American Physical Society, 2022."},"acknowledgement":"M.L. and T.P. acknowledge support from the European Research Council (ERC) through the advanced grant 694544 – OMNES and the grant P1-0402 of Slovenian Research Agency (ARRS). M.L. acknowledges support from the European Research Council (ERC) through the starting grant 850899 – NEQuM. D.R. acknowledges support from the Ministry of Electronics & Information Technology (MeitY), India under the grant for “Centre for Excellence in Quantum\r\nTechnologies” with Ref. No. 4(7)/2020-ITEA. ","publication_identifier":{"eissn":["2469-9969"],"issn":["2469-9950"]},"doi":"10.1103/physrevb.106.054314","day":"31","_id":"12269","issue":"5","publication":"Physical Review B","volume":106,"ec_funded":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_number":"054314","article_type":"original","date_created":"2023-01-16T10:00:39Z","external_id":{"arxiv":["2106.08373"],"isi":["000861332900005"]},"author":[{"first_name":"Marko","full_name":"Ljubotina, Marko","last_name":"Ljubotina","orcid":"0000-0003-0038-7068","id":"F75EE9BE-5C90-11EA-905D-16643DDC885E"},{"last_name":"Roy","first_name":"Dibyendu","full_name":"Roy, Dibyendu"},{"full_name":"Prosen, Tomaž","first_name":"Tomaž","last_name":"Prosen"}],"language":[{"iso":"eng"}],"intvolume":"       106","status":"public","title":"Absence of thermalization of free systems coupled to gapped interacting reservoirs","quality_controlled":"1"}]